The Experts below are selected from a list of 30600 Experts worldwide ranked by ideXlab platform
Brett T Litz - One of the best experts on this subject based on the ideXlab platform.
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co occurring posttraumatic stress and depression symptoms after sexual assault a latent profile analysis
Journal of Affective Disorders, 2013Co-Authors: Benjamin D Dickstein, Jonathan S Comer, Kristalyn Salterspedneault, Brett T LitzAbstract:BACKGROUND: Symptoms of posttraumatic stress disorder (PTSD) and depression frequently co-occur, but their distinctiveness following trauma remains unclear. We examined patterns of PTSD and depression symptoms after sexual assault to evaluate the extent to which assault survivors primarily reported symptoms of both disorders or whether there were meaningfully distinct subgroups with discordant PTSD and depression symptoms. METHODS: Latent profile analysis was used to examine self-reported PTSD and depression symptoms among 119 female sexual assault survivors at 1-, 2-, 3-, and 4-months post-assault. RESULTS: At all time points, a 4-Class solution fit the data best, revealing four subgroups with low, low-moderate, high-moderate, and severe levels of both PTSD and depression symptoms. Within each subgroup, PTSD symptom severity co-occurred with comparable depression symptom severity. At no time point were there reliable subgroups with discordant PTSD and depression symptom severities. Emotional numbing, hyperarousal, and overall PTSD symptom severity reliably distinguished each Class from the others. Class Membership at 1-month post-assault predicted subsequent Class Membership and functional impairment. LIMITATIONS: Additional research is needed to evaluate predictors of Class Membership, temporal stability of Classes, and generalizability to other trauma populations. CONCLUSIONS: Co-occurring and comparably severe PTSD and depression symptoms are pervasive among female sexual assault survivors. The absence of a distinct subset of individuals with only PTSD or depression symptoms suggests that PTSD and depression may be manifestations of a general posttraumatic stress response rather than distinct disorders after trauma. Integrated treatments targeting both PTSD and depression symptoms may therefore prove more efficient and effective. Language: en
Thierry Denoeux - One of the best experts on this subject based on the ideXlab platform.
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Dissimilarity Metric Learning in the Belief Function Framework
IEEE Transactions on Fuzzy Systems, 2016Co-Authors: Chunfeng Lian, Su Ruan, Thierry DenoeuxAbstract:The Evidential K-Nearest-Neighbor (EK-NN) method provided a global treatment of imperfect knowledge regarding the Class Membership of training patterns. It has outperformed traditional K-NN rules in many applications, but still shares some of their basic limitations, e.g., 1) Classification accuracy depends heavily on how to quantify the dissimilarity between different patterns and 2) no guarantee for satisfactory performance when training patterns contain unreliable (imprecise and/or uncertain) input features. In this paper, we propose to address these issues by learning a suitable metric, using a low-dimensional transformation of the input space, so as to maximize both the accuracy and efficiency of the EK-NN Classification. To this end, a novel loss function to learn the dissimilarity metric is constructed. It consists of two terms: the first one quantifies the imprecision regarding the Class Membership of each training pattern; while, by means of feature selection, the second one controls the influence of unreliable input features on the output linear transformation. The proposed method has been compared with some other metric learning methods on several synthetic and real data sets. It consistently led to comparable performance with regard to testing accuracy and Class structure visualization.
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a k nearest neighbor Classification rule based on dempster shafer theory
Classic Works of the Dempster-Shafer Theory of Belief Functions, 2008Co-Authors: Thierry DenoeuxAbstract:In this paper, the problem of Classifying an unseen pattern on the basis of its nearest neighbors in a recorded data set is addressed from the point of view of Dempster-Shafer theory. Each neighbor of a sample to be Classified is considered as an item of evidence that supports certain hypotheses regarding the Class Membership of that pattern. The degree of support is defined as a function of the distance between the two vectors. The evidence of the k nearest neighbors is then pooled by means of Dempster's rule of combination. This approach provides a global treatment of such issues as ambiguity and distance rejection, and imperfect knowledge regarding the Class Membership of training patterns. The effectiveness of this Classification scheme as compared to the voting and distance-weighted k-NN procedures is demonstrated using several sets of simulated and real-world data. >
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a neural network Classifier based on dempster shafer theory
Systems Man and Cybernetics, 2000Co-Authors: Thierry DenoeuxAbstract:A new adaptive pattern Classifier based on the Dempster-Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the Class Membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the Class Membership of each prototype are determined by minimizing the mean squared differences between the Classifier outputs and target values. After training, the Classifier computes for each input vector a BBA that provides a description of the uncertainty pertaining to the Class of the current pattern, given the available evidence. This information may be used to implement various decision rules allowing for ambiguous pattern rejection and novelty detection. The outputs of several Classifiers may also be combined in a sensor fusion context, yielding decision procedures which are very robust to sensor failures or changes in the system environment. Experiments with simulated and real data demonstrate the excellent performance of this Classification scheme as compared to existing statistical and neural network techniques.
Benjamin D Dickstein - One of the best experts on this subject based on the ideXlab platform.
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co occurring posttraumatic stress and depression symptoms after sexual assault a latent profile analysis
Journal of Affective Disorders, 2013Co-Authors: Benjamin D Dickstein, Jonathan S Comer, Kristalyn Salterspedneault, Brett T LitzAbstract:BACKGROUND: Symptoms of posttraumatic stress disorder (PTSD) and depression frequently co-occur, but their distinctiveness following trauma remains unclear. We examined patterns of PTSD and depression symptoms after sexual assault to evaluate the extent to which assault survivors primarily reported symptoms of both disorders or whether there were meaningfully distinct subgroups with discordant PTSD and depression symptoms. METHODS: Latent profile analysis was used to examine self-reported PTSD and depression symptoms among 119 female sexual assault survivors at 1-, 2-, 3-, and 4-months post-assault. RESULTS: At all time points, a 4-Class solution fit the data best, revealing four subgroups with low, low-moderate, high-moderate, and severe levels of both PTSD and depression symptoms. Within each subgroup, PTSD symptom severity co-occurred with comparable depression symptom severity. At no time point were there reliable subgroups with discordant PTSD and depression symptom severities. Emotional numbing, hyperarousal, and overall PTSD symptom severity reliably distinguished each Class from the others. Class Membership at 1-month post-assault predicted subsequent Class Membership and functional impairment. LIMITATIONS: Additional research is needed to evaluate predictors of Class Membership, temporal stability of Classes, and generalizability to other trauma populations. CONCLUSIONS: Co-occurring and comparably severe PTSD and depression symptoms are pervasive among female sexual assault survivors. The absence of a distinct subset of individuals with only PTSD or depression symptoms suggests that PTSD and depression may be manifestations of a general posttraumatic stress response rather than distinct disorders after trauma. Integrated treatments targeting both PTSD and depression symptoms may therefore prove more efficient and effective. Language: en
I-fan Shen - One of the best experts on this subject based on the ideXlab platform.
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supervised local tangent space alignment for Classification
International Joint Conference on Artificial Intelligence, 2005Co-Authors: Hongyu Li, Wenbin Chen, I-fan ShenAbstract:Supervised local tangent space alignment (SLTSA) is an extension of local tangent space alignment (LTSA) to supervised feature extraction. Two algorithmic improvements are made upon LTSA for Classification. First a simple technique is proposed to map new data to the embedded low-dimensional space and make LTSA suitable in a changing, dynamic environment. Then SLTSA is introduced to deal with data sets containing multiple Classes with Class Membership information.
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supervised learning on local tangent space
International Symposium on Neural Networks, 2005Co-Authors: Hongyu Li, Li Teng, Wenbin Chen, I-fan ShenAbstract:A novel supervised learning method is proposed in this paper. It is an extension of local tangent space alignment (LTSA) to supervised feature extraction. First LTSA has been improved to be suitable in a changing, dynamic environment, that is, now it can map new data to the embedded low-dimensional space. Next Class Membership information is introduced to construct local tangent space when data sets contain multiple Classes. This method has been applied to a number of data sets for Classification and performs well when combined with some simple Classifiers.
Natacha Carragher - One of the best experts on this subject based on the ideXlab platform.
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adolescent predictors of a typology of dsm 5 alcohol use disorder symptoms in young adults derived by latent Class analysis using data from an australian cohort study
Journal of Studies on Alcohol and Drugs, 2016Co-Authors: Wendy Swift, Tim Slade, Natacha Carragher, Carolyn Coffey, Louisa Degenhardt, Wayne HallAbstract:Objective:There is little research examining alcohol use disorder (AUD) symptoms (based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]) in young adulthood. We assessed symptom structure at 24 years using latent Class analysis (LCA), examining relationships between Class Membership and (a) concurrent alcohol use and DSM-5 AUD severity and (b) adolescent risk factors.Method:A stratified, random sample of 1,943 adolescents ages 14–15 years was recruited from 44 secondary schools in Victoria, Australia, and interviewed during adolescence and young adulthood. We report findings on drinkers who completed the AUD module (N = 1,268; 51% male).Results:Data clearly fit a three-Class, dimensional model, comprising “mild symptoms” (63.2%), “moderate symptoms” (32.2%), and “severe symptoms” (4.6%) Classes. Class Membership was validated by concurrent drinking patterns and in reasonable agreement with DSM-5 AUD severity categories. Relative to mild symptoms Class membe...